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Current issue

ELEKTRO 12/2016 was released on December 7th 2016. Its digital version will be available on January 6th 2017.

 

Topic: Measurement, measuring devices and measurement engineering; Testing and diagnostics

 

Main Article

Lithium traction batteries for electric mobility (final part 2)

SVĚTLO (Light) 6/2016 was released on December 5th 2016. Its digital version will be available on January 5th 2017.

Interiors lighting
Colloquium Interiors 2016 – the fifth anniversary
Cooperation of indoor interior and lighting 

Standards, regulations and recommendations
New standards for road lighting

Getting More Miles From Plug-in Hybrids

17.02.2016 | University of California | ucrtoday.ucr.edu

Plug-in hybrid electric vehicles (PHEVs) can reduce fuel consumption and greenhouse gas emissions compared to their gas-only counterparts. Researchers at the University of California, Riverside’s Bourns College of Engineering have taken the technology one step further, demonstrating how to improve the efficiency of current PHEVs by almost 12 percent.

Since plug-in hybrids combine gas or diesel engines with electric motors and large rechargeable batteries, a key component is an energy management system (EMS) that controls when they switch from ‘all-electric’ mode, during which stored energy from their batteries is used, to ‘hybrid’ mode, which utilizes both fuel and electricity. As new EMS devices are developed, an important consideration is combining the power streams from both sources in the most energy-efficient way.

Better efficiency of hybrid systems

While the UCR EMS does require trip-related information, it also gathers data in real time using onboard sensors and communications devices, rather than demanding it upfront. It is one of the first systems based on a machine learning technique called reinforcement learning (RL).

In comparison-based tests on a 20-mile commute in Southern California, the UCR EMS outperformed currently available binary mode systems, with average fuel savings of 11.9 percent. Even better, the system gets smarter the more it’s used and is not model- or driver-specific, meaning it can be applied to any PHEV driven by any individual.

Read more at University of California

Image Credit: Wikipedia

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